The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Recent advancements in the field of media processing have led to development of various algorithms for detection of compression artifacts in an image. Such techniques for detection of compression artifacts may be spatial domain based or frequency domain based. The compression artifacts may be attributed to various types of lossy compression techniques applied on the image for compression, wherein data is quantized and/or discarded. The artifacts may correspond to ringing, blockiness, and/or the like. The ringing artifacts may result in an appearance of spurious objects near the edges of one or more objects present in the image. The blockiness artifacts may be caused by use of block-based transforms for compression. Such block-based transforms may result in pixelation (macroblocking) in the image when the bit rate is low.
In certain scenarios, the occurrence of artifacts may be attributed to high quantization, e.g. high quantization parameter (QP) used in frequency transform (e.g. DCT) coefficient quantization. A high QP value may lead to an attenuation of high frequency (HF) components of the image in a transform domain. Consequently, due to the high QP value, the probability of HF components being quantized to a programmable threshold may increase, thereby leading to introduction of compression artifacts in the image that may be rendered.
In one of the techniques, a local blur is detected at macroblock edges and a content based weighting scheme is employed to reduce the affect from texture. In accordance with another technique, blocks in the image that are affected by ringing are detected based on a block grid position, location of an object edge and comparison of the local spatial activities of adjacent blocks (affected by artifacts) with those that are not-affected. In accordance with another technique, ringing artifacts in digital images are predicted by determining a number of pixels of a blocks exhibiting luminance exceeding a luminance threshold adequate for detection of a ringing artifact, and a number of such higher luminance pixels that have sufficient contrast with neighbour pixels to be a likely source of a ringing artifact. Other techniques for compression artifacts detection are based on analysis of the edges and adjacent regions in an image that correspond to very low computational complexity.
The existing methods of compression artifact detection are not robust enough as they do not exhaustively explore the combination of various image parameters in detecting the compression artifacts. In order to render high quality media content, it is imperative to minimise the compression artifacts. This in turn, requires robust detection of the various types of compression artifacts and their cause, so as to modify the parameters that lead to compression artifact minimisation.